Noise-robust classification of single-shot electron spin readouts using a deep neural network

TL;DR

This paper introduces a deep neural network-based method for single-shot electron spin readout that maintains high accuracy even in noisy environments, improving the robustness of quantum dot charge sensing.

Contribution

The study develops a novel DNN classifier that automatically adapts to noisy conditions for spin state detection, outperforming conventional methods.

Findings

DNN classifier achieves higher accuracy in noisy environments.

The method is automatically configured for different noise levels.

Robustness is validated against traditional classification techniques.

Abstract

Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. The fidelity of the single-shot readout depends both on experimental conditions such as signal-to-noise ratio, system temperature and numerical parameters such as threshold values. Accurate charge sensing schemes that are robust under noisy environments are indispensable for developing a scalable fault-tolerant quantum computation architecture. In this study, we present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN). Importantly, the DNN classifier is automatically configured for spin-up and spin-down signals in any noise environment by tuning the trainable parameters using the datasets of charge transition signals experimentally obtained at a charging line. Moreover, we verify that our DNN classification is robust under noisy environment in comparison to the two conventional classification methods used for charge and spin state measurements in various quantum dot experiments.